Linear contextual bandits represent a fundamental class of models with numerous real-world applications, and it is critical to developing algorithms that can effectively manage noise with unknown variance, ensuring provable guarantees for both worst-case constant-variance noise and deterministic reward scenarios.

Rare-events data refer to binary-response data that are highly imbalanced, i.e., the number of zeros

Different distribution shifts require different algorithmic and operational interventions. Methodological research must be grounded by the specific shifts they address.